Rationalization Of Industrial Structure Research Articles

Heterogeneity Analysis of Industrial Structure Upgrading on Eco-Environmental Quality from a Spatial Perspective: Evidence from 11 Coastal Provinces in China

Upgrading the industrial structure and improving the quality of the ecological environment are important strategic steps to realize the modernization of China. Based on the panel data of 11 provinces (municipalities) in China’s coastal areas from 2010 to 2019, this paper uses the spatial Dubin model and the threshold effect model to study the impact of industrial structure upgrading on eco-environmental quality. The results show that the influence of industrial structure upgrading on ecological environment quality has a positive “U”-shaped distribution. Based on the spatial econometric model, it is found that the rationalization of industrial structure and the optimization of industrial structure have spatial spillover effects on the ecological environment quality, and the influence of the rationalization of industrial structure and the optimization of industrial structure on the ecological environment quality of the surrounding area is positive “U”-shaped and inverted “U”-shaped, respectively. Based on the threshold model, it is found that industrial structure rationalization has a small effect on the ecological environment’s quality when the degree of scientific and technological innovation is low. When scientific and technological innovation reaches a certain threshold, industrial structure rationalization has a significant effect on the quality of the ecological environment. In addition, from a regional perspective, the influence of industrial structure rationalization in the East China Sea and the South China Sea and industrial optimization in the Bohai-Yellow Seas on the eco-environmental quality of the surrounding areas has a positive “U”-shaped distribution, while the influence of the optimization of industrial structure in the South China Sea on the eco-environmental quality of the surrounding areas has an inverted “U”-shaped curve on the left side.

Asymmetric effects of industrial structure rationalization on carbon emissions: Evidence from thirty Chinese provinces

This paper aims to study the significance of China's Industrial Structure Rationalization (ISR) in reducing carbon emissions. To this end, this study uses panel data from 30 Chinese province from 2000 to 2019 to measure the Industrial Structure Rationalization of each province by calculating the Thiel index, base on which a nonlinear autoregressive lagged distribution (NARDL-PMG) model is established to explore the impacts of industrial structure rationalization on carbon emissions. The results show that: (1) There still exist significant regional differences between Eastern and Central-Western regions in industrial structure as illustrated by the ISR index. The industrial structure in Eastern region is more rational; (2) In the long run, the carbon emission in China will drop when the industrial structure becomes more rational; (3) However, in the short run, the carbon emission in China will increase when the industrial structure becomes more rational. Besides the findings above, the major difference between this paper and the extant researches is that this paper pays attention to the relationship between industrial structure rationalization and environmental quality by investigating the asymmetric influences of industrial structure rationalization index's positive and negative shocks on carbon emission, which lays solid foundations for government aiming to set more detailed policies.

How Does Industrial Upgrading Affect Urban Ecological Efficiency? New Evidence from China

ABSTRACT The upgrade of industrial structure is one type of momentum for improving urban ecological efficiency, and exploring the nexus between them can help implement the concept of green development. Using data from 284 cities in China between 2004 and 2019, this research employs a series of econometric models to examine how improving industrial structure contributes to the growth of urban ecological efficiency. The research results reveal that urban ecological efficiency can be significantly improved by industrial structure upgrade, while ecological efficiency is inhibited by low-quality GDP and financial development models and insignificantly decreased by industrial structure rationalization. In addition, industrial structure upgrading has failed to play a mediating role in promoting eco-efficiency through technological innovation and the reduction of pollutant emission intensity. Moreover, officials’ turnover plays a negative moderating influence on the role of industrial upgrading in increasing ecological efficiency, while environmental regulation plays the opposite role. This study further conducts triple heterogeneity tests including city size, resource endowment, and regional heterogeneity, which provide a feasible way for further implementation of relevant policies.

Trade Opening and The Real Effective Exchange Rate of the RMB

This paper uses the Redux model and shows that the fluctuation of the RMB under certain fluctuations; then analyzes the effect of the fluctuation of the RMB on the actual effective exchange rate. The study found that trade opening can reduce the fluctuation of the real effective exchange rate of the RMB; the rationalization of industrial structure can also enhance trade opening and restrain the real effective exchange rate fluctuation of the RMB. Meanwhile, the "Belt and Road" initiative intensifies the real effective exchange rate fluctuation of the RMB, possibly due to the change of the "Belt and Road" initiative and implementation in the total industrial structure of import and export. Therefore, all provinces (autonomous regions and municipalities directly under the Central Government) should increase basic research such as science and technology, promote the rationalization of industrial structure, and make the help of the good opportunity of the "Belt and Road" initiative to reduce the real effective exchange rate fluctuations of RMB.

Has the ecological civilization pilot promoted the transformation of industrial structure in China?

Industrial structure transformation is important for realizing Chinese modernization, and ecological civilization is necessary for building a beautiful China. How to use the construction of ecological civilization to realize industrial structure transformation is a major issue for the economic development of China and other developing countries. The difference-in-differences method was used to evaluate the impact of the ecological civilization pilot policy (ECPP) on industrial structure transformation. The three-stage mediating effect method is used to explore the mechanism channel. Through heterogeneity discussion and spatial spillover analysis method, the effects of ECPP were further understood. The study reveals the following: Overall, the ECPP has promoted the transformation of the industrial structure. Specifically, the ECPP has a significant positive impact on industrial structure rationalization but a negative impact on industrial structure advancement. Several robustness checks support this finding. Second, the mechanism channels are primarily achieved through environmental investment, green finance infusion, and congestion alleviation. Third, good central-local relationships can enhance the trend toward industrial structure rationalization, while unfavorable relationships may pose hindrances to industrial structure advancement. In addition, the effect of the ECPP varies depending on the population density and environmental pollution. Lastly, the ECPP has a spatial positive spillover effect promoting industrial structure rationalization. The results above provide additional information about the effects of ECPPs on industrial structure transformation, both positive and negative, and help the ecological civilization strategy system be adjusted promptly.

Urban economic resilience and human capital: An exploration of heterogeneity and mechanism in the context of spatial population mobility

Human capital is a critical factor that affects urban economic resilience (UER). We utilize Baidu migration big data to construct a population mobility matrix and provide an analysis of the mechanism of UER from four different perspectives: innovation, industrial structure advancement, industrial structure rationalization, and economic development. Our findings suggest that human capital contributes to approximately 7 % improvement in UER through innovation and economic development, while the role of industrial structure advancement and rationalization is not significant. To address potential endogeneity, we employ an instrumental variable (IV) using spatial two-stage least squares. Additionally, we conduct a heterogeneity analysis based on the city tier, human capital level, and UER size. We replenish the study on UER and offer feasible solutions from the perspective of human capital to enhance UER.

Understanding the efficiency and evolution of China's Green Economy: A province-level analysis

The efficiency level, evolution characteristics, and factors driving the green economy in all provinces and regions should be clarified to achieve high-quality economic development and meet China's “double carbon” target. This study conducted the Super-Effective Slack-Based Model considering unexpected outputs to evaluate province-level Green Economic Efficiency (GEE) analysis (including 30 provinces, autonomous regions, and municipalities directly under the Central Government) in China from 2005 to 2020. Moreover, the distribution and dynamic evolution trend of GEE development was estimated through Kernel density estimation. Besides, GEE and its factors (i.e., industrial structure rationalization [ISR], industrial structure advancement [ISA], and urbanization level [UL]) were examined using a Panel vector autoregressive model that was built in this study. As indicated by the result of this study, China's GEE level generally displayed a “U-shaped” development trend of declining, stabilizing, and then rising, whereas the overall efficiency level is low, where the national GEE average reached 0.6934. The regional GEE level exhibited a significant “ladder” distribution, with the highest level, the second level, and the lowest level in the east, the middle, and the west, respectively. The GEE level varied significantly with the province, and most of the levels were at a medium efficiency level. Notably, 60% of regions had medium efficiency in 2020. The levels of ISR, ISA, and UL play significant roles in boosting green economic growth. This study provides valuable insights into the drivers of green economic growth in China guiding policy decisions on achieving a sustainable and low-carbon economy. As China strives to fulfill its ambitious carbon reduction goals, the findings of this study highlight the significance of continuing to prioritize green economic development at the provincial level.

Does Digital Infrastructure Improve Urban Economic Resilience? Evidence from the Yangtze River Economic Belt in China

Under the pressure exerted by COVID-19 and geopolitical conflicts, establishing how to enhance urban economic resilience and cope with external risks has become the focus of many studies. This study considers the “Broadband China” program as a quasi-natural experiment and uses panel data from 106 Chinese cities between 2011 and 2020 to explore the influence of digital infrastructure on urban economic resilience through a difference-in-differences (DID) approach. The results are as follows: (1) Digital infrastructure improves urban economic resilience, and the influence differs by time and region. (2) Economic vitality, industrial structure upgrading, and industrial structure rationalization either moderate or mediate the impact of the digital infrastructure on economic resilience. Our findings contribute to a better understanding of how digital infrastructure and economic resilience are related.

Artificial intelligence, industrial structure optimization, and CO2 emissions.

The carbon-reducing effects of artificial intelligence (AI) will be a critical means of achieving carbon peak and carbon neutrality in China. However, in order to efficiently harness the power of AI, the relationship between AI and carbon reduction needs to be fully understood. In this study, we systematically investigated the impacts and mechanisms of action of AI on CO2 emissions by constructing econometric models using dynamic panel data from 30 provinces in mainland China from 2006 to 2019. The empirical results show that AI significantly reduces CO2 emissions. Further mediation effect tests found that in the western region, there are mediation effects of the quantity and quality of industrial structure advancedization and industrial structure ecology, while the mediation effect of industrial structure rationalization is not significant. In the eastern and central regions, the mediating effect of the quantity of industrial structure advanced is not significant, while the mediating effect of the quality of industrial structure advanced, industrial structure rationalization, and industrial structure ecology all exist. Our work provides evidence to support that AI reduces CO2 emissions in various regions of China. This can help regions formulate appropriate policies to promote the synergistic development of AI and the "dual-carbon" goal.

Digital economy, credit expansion, and modernization of industrial structure in China

Digital economy promotes the modernization of industrial structure by influencing the rationalization and upgrading of industrial structure through technical level and factor level; while excessive credit expansion hinders the modernization of industrial structure. This paper uses panel data from 31 jurisdictions in China to conduct empirical analysis, and finds that digital economy development shows a year-on-year rising trend, and there is a large gap between different regions. The conclusion still holds after the robustness test and regional heterogeneity analysis, thus enriching the understanding of mechanisms and regional differentiation of digital economy, credit expansion on industrial structure modernization.

Impact of Industrial Structure Rationalization and Upgrading on Well-Being Performance of Carbon Emissions- Empirical Analysis Based on Chinese Provincial Level

Impact of Industrial Structure Rationalization and Upgrading on Well-Being Performance of Carbon Emissions- Empirical Analysis Based on Chinese Provincial Level

The impact of green taxation on green low-carbon development in Yangtze River Delta region of China-based on mediating effect model and spatial Durbin model.

Promoting green low-carbon development is an important task in the construction of ecological civilization of China. The leadership has proclaimed green low-carbon development as the top national priority for development. Green low-carbon economy has become the high point of global industrial competition. Green taxation, as a tool for environmental regulation, plays an important role in the construction of ecological civilization. Therefore, this paper investigates the impact of green taxation on green low-carbon development in the Yangtze River Delta (YRD) region of China, using data of 41 cities from 2011 to 2021 in the YRD region. We construct a green low-carbon development index with the combined weight CRITIC-entropy method, and employ a mediating effect model and a spatial Durbin model based on double fixed effects in this study, which intends to provide a theoretical basis for the establishment of a sound tax policy system that is conducive to the green low-carbon development. The results show that (1) there is an evident spatial dependence of green low-carbon development in the YRD region; (2) green taxation has a significant positive effect on green low-carbon development through rationalization of industrial structure and advance of industrial structure; and (3) the positive impact of green taxation on green low-carbon development in the region is characterized by spatial spillover and heterogeneity. Specifically, green taxation significantly promotes green low-carbon development in the core area, but such green taxation dividend has yet to be realized in the expansion area.

Low-carbon policy and industrial structure upgrading: Based on the perspective of strategic interaction among local governments

China has continuously implemented the low-carbon policy in response to global climate change. Guiding the low-carbon development of economy and achieving the upgrading of industrial structure have become important way to achieve carbon peaking and carbon neutrality. Using panel data of 30 provinces in China from 2006 to 2019, this paper constructed spatial Durbin model and explored the impact of low-carbon policy on industrial structure upgrading from the perspective of strategic interaction. The results show that the strategic interaction form of local governmental low-carbon policy is a “race to the top”. Under the intergovernmental strategic interaction, the low-carbon policy not only significantly improve the level of rationalization and supererogation of the local industrial structure, but also industrial structure of spatially related regions. The heterogeneity test shows that there are temporal and regional differences in the impact of low-carbon policy on industrial structure upgrading. Specifically, the impact of low-carbon policy on industrial structure rationalization is the most evident in the western region, while the impact on industrial structure supererogation is the greatest in the eastern region. Furthermore, market segmentation inhibits the impact of low-carbon policy on industrial structure upgrading, and technological progress strengthens the impact.

Spatial Effect of Green Economic Efficiency in China From the Perspective of Informatization

Green economy has been paid more and more attention in the information age. Informatization plays an important role in the development of green economy by the transmission of industrial structure rationalization and upgrading. Because of the spatial mobility of information, it is necessary to study the spatial spillover effect of information on the efficiency of green economy. In this article, the non-radial directional distance function and the comprehensive index method are used to evaluate the efficiency of green economy and informatization respectively. On this basis, the spatial characteristics of the two are analyzed. Finally, the spatial econometric model is used to analyze the spatial impact of informatization on the efficiency of green economy. The authors find that: (i)The spatial distribution of the green economy efficiency and informatization are unbalanced; (ii) There is a significant spatial spillover effect in the efficiency of green economy; and (iii) The development of informatization plays an important impact on the efficiency of green economy.

How does science and technology innovation improve carbon productivity?-Evidence at China's province level.

Science and technological innovation play a crucial role in achieving the goals of carbon peaking and carbon neutrality. They are also vital for improving the scale, structure, and allocation of input and output factors, as well as enhancing the marginal efficiency of production factors. This study investigates the impact of science and technological innovation on carbon productivity using data from 30 provinces (cities, and autonomous regions) in China spanning the period from 2004 to 2020. The findings are as follows: First, the spatial characteristics of carbon productivity in China have gradually evolved into "low in the northern regions and high in the southern regions." The spatial distribution of science and technological innovation in China shows a gradual decrease from the southeast to the northwest. Second, employing the fixed effect model, we find that science and technological innovation has a significant positive effect on carbon productivity, with regional variations in the magnitude of the effect. Furthermore, we identify industrial structure rationalization, industrial structure advancement, and industrial structure ecology as important mechanisms through which science and technological innovation influence carbon productivity. The mediation effects are measured at 3.37%, 8%, and 73.94%, respectively. These research findings provide valuable insights for enhancing technological innovation capabilities, realizing low-carbon development, and addressing the challenges posed by climate change.

How does Financial Development Affect Regional Economic Growth in China? A Mediating Role of Industrial Structure Optimization

<p style="text-align: justify;">Financial development and industrial structure optimization are two-wheeled engines for promoting high-quality development, but the causal identification of the two is still poorly understood. This paper addresses this research gap by comprehensively analyzing the relationship between them. Regional economic growth is a necessary condition for achieving high-quality development. Taking China as an example, this study empirically examines the role of financial development and industrial structure optimization in high-quality development using the entropy method, fixed effects, mediation effects and instrumental variable regression. The study results show that (i) financial development has actively promoted regional economic growth. (ii) The driving effect on the central and western regions is greater than that on the eastern region. (iii) There are differences in the mediating effect of industrial structure optimization and rationalization between different levels of financial development and economic growth. (iv) In addition, the mediating effect of industrial structure optimization is larger in the eastern region than in the central and western regions. (v) The mediating effect of industrial structure rationalization is not obvious in the eastern region, but has a masking effect in the central and western regions. Our findings provide new strategies for financial development and industrial structure optimization in developing countries.</p>

The Impact of the Digital Economy on the Optimization of Industrial Structure: An Empirical Study Based on the Intermediary Role of Technological Innovation

The digital economy has become an important path for the transformation and upgrading of industrial structure. Technological innovation, as the primary driving force for development, is a key entry point in analyzing this issue. This paper conducts empirical analysis based on Panel data of 31 provinces in China from 2011-2021, and constructs Mesomeric effect and moderating effect models. Research has found that the development of the digital economy has a significant promoting effect on the rationalization of industrial structure; The digital economy can promote the improvement of technological innovation level; From a national perspective, the level of technological innovation reflects the masking effect in the process of promoting industrial structure rationalization in the digital economy, and this conclusion is still valid after robustness testing.Based on this, we need to strengthen the construction of digital infrastructure, rely on the digital economy to stimulate independent innovation and technological transformation level, and promote the upgrading of industrial structure.

The impact of industrial structure adjustment on the spatial industrial linkage of carbon emission: From the perspective of climate change mitigation

Industrial structure adjustment is an effective way to reduce carbon emissions. When adjusting the industrial structure, changes in the input and output of industrial sectors in one region may cause changes in industrial sectors of other regions, resulting in inter-regional industrial linkages. What is the impact of industrial structure adjustment on the linkages, and how does it impact climate change? The modified gravity model combines the influence and inductive coefficients to build the carbon emission industrial spatial linkage network (ISL) using social network analysis (SNA) in this work with the data of China's 2008–2019 provincial carbon emissions. The impact of industrial structure adjustment on carbon emission industrial spatial linkage network and its effect on climate change mitigation are explored with the help of the STIRPAT model and quality assurance procedure (QAP). The results of this study are as follows: (1) The provincial carbon emissions under industrial spatial linkages show significant network characteristics, the network density increased from 0.1437 in 2008 to 0.323 in 2019. (2) There is a significant spatial spillover effect in the industrial spatial linkage network (2019) centered on Shandong and Jiangsu. The provinces can be divided into 4 blocks, which play different roles in the network. (3) The industrial structure upgrading can significantly promote the carbon emission industrial linkage network and mitigate climate change. (4) The industrial structure rationalization can promote the development of the network and mitigate climate change in the early and late development stages of carbon emissions industrial linkage. In addition, it is necessary for the government to pay more attention to the development level of the carbon emission spatial industrial linkage network when implementing industrial structure adjustment in response to climate change.

How Does the Digital Economy Affect Green Development?—Evidence from 284 Cities in China

The expansion of the digital economy has resulted in extensive changes to production factors, production methods, and lifestyles, making it a key factor in green development. In a unified framework, this paper examines the impact of China’s digital economy on green development and the transmission mechanisms of the digital economy. Based on a theoretical analysis of the green attributes and transmission mechanisms of the digital economy, the relationship is empirically examined using the fixed effects model, the instrumental variables method, the quantile regression model, and the mediating effects model with China-specific data from 2011 to 2019. The results indicate that the digital economy has a significant positive impact on green development, and that this impact grows as GTFP (Green Total Factor Productivity) increases. The digital economy has a lasting impact. According to the analysis of heterogeneity, the impact of the digital economy varies significantly between regions, and this disparity exists in both small and large cities. The “digital gap” between high- and low-level cities exacerbates the disparity in the digital economy’s effects. The mechanism analysis reveals that industrial structure rationalization and environmental improvement are the primary means by which the digital economy’s effects are transmitted. Currently, the “innovation-to-application” conversion efficiency is low, and the “demand expansion effect” is greater than the “efficiency enhancement effect”, which impedes the transmission path of green technology innovation and energy use efficiency. The research findings serve as a guide for promoting the development of Digital China and accelerating the green transformation.

The impact of the digital economy on the urban-rural income gap: Research based on the rationalization of the industrial

The booming digital economy will affect the income gap between urban and rural areas in China. Based on the provincial data from 2011 to 2021, this paper selects a fixed-effect model to empirically test the impact of digital economy on the urban and rural income gap in China and the adjustment effect of the rationalization of industrial structure. The research shows that digital economy can significantly improve the urban-rural income gap, while the rationalization of industrial structure will expand the gap and reduce the effect of digital economy on the urban-rural income gap; Regional heterogeneity test shows that the effect of digital economy on the urban-rural income gap, and the marginal effect of digital economy on the urban-rural income gap is less than that in the central and western regions. Therefore, the government should vigorously develop the rural digital economy, pay attention to the training and introduction of rural digital technology talents, and implement the differentiated digital economy development strategy according to local conditions, so as to better realize the role of digital economy in improving the urban and rural income gap in China.